Whale optimization algorithm: a systematic review of contemporary applications, modifications and developments

Rana, Nadim; Latiff, Muhammad Shafie Abd; Abdulhamid, Shafií Muhammad; Chiroma, Haruna

doi:10.1007/s00521-020-04849-z

Cited by 178 publications

(60 citation statements)

References 77 publications

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“…A whale based optimization [1], [34] is used on the weight values of the DLTPM for faster synchronization. As the optimal weights contribute to faster convergence, a greater weight range can be taken into consideration to enhance the security of existing TPM.…”

Section: Proposed Methodology a Optimal Weight Vector Generation Algorithmmentioning

confidence: 99%

Artificial Neural Synchronization Using Nature Inspired Whale Optimization

et al. 2021

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In this article, a whale optimization-based neural synchronization has been proposed for the development of the key exchange protocol. At the time of exchange of sensitive information, intruders can effortlessly perform sniffing, spoofing, phishing, or Man-In-The-Middle (MITM) attack to tamper the vital information. Information needs to be secretly transmitted with high level of encryption by preserving the authentication, confidentiality, and integrity factors. Such stated requirements urge the researchers to develop a neural network-based fast and robust security protocol. A special neural network structure called Double Layer Tree Parity Machine (DLTPM) is proposed for neural synchronization. Two DLTPMs accept the common input and different weight vectors and update the weights using neural learning rules by exchanging their output. In some steps, it results in complete synchronization, and the weights of the two DLTMs become identical. These identical weights serve as a secret key. There is, however, hardly any research in the field of neural weight vector optimization using a nature-inspired algorithm for faster neural synchronization. In this article, whale optimization-based DLTPM is proposed. For faster synchronization, this proposed DLTPM model uses a whale algorithm optimized weight vector. This proposed DLTPM model is faster and has better security. This proposed technique has been passed through a series of parametric tests. The results have been compared with some recent techniques. The results of the proposed technique have shown effective and has robust potential.The associate editor coordinating the review of this manuscript and approving it for publication was Jiafeng Xie.secret while he may be able to follow the structure of the algorithm. Chen et al. [2]; Liu et al. [3]; Chen et al. [4]; Wang et al. [5], [6]; Xiao et al. [7]; Zhang and Cao [8]; Wang et al. [9]; Dong et al. [10] described that the neural network synchronization approach offers the chance to solve enormous exchange problems. Rosen-Zvi et al. [11]; Lakshmanan et al. [12] ; Ni and Paul [13] recently showed that neural cryptography has the capability of achieving key exchange through neural synchronization of Artificial Neural Networks (ANN). This proposed technique uses an ANN called the Double Layer Tree Parity Machine (DLTPM). By generating common inputs and sharing the outputs of such networks,

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Section: Proposed Methodology a Optimal Weight Vector Generation Algorithmmentioning

confidence: 99%

Artificial Neural Synchronization Using Nature Inspired Whale Optimization

et al. 2021

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“…The effort is made to identify the best search agent, while the other search agents will update their positions near to the best search agent. The behavior is expressed by the following equations as stated by [56]:…”

Section:  the Whale Optimization Algorithm (Woa)mentioning

confidence: 99%

A Review on Path Planning Algorithms for Mobile Robots

Abed¹,

Lutfy²,

Al-Doori³

2021

ETJ

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Mobile robots use is rising every day. Path planning algorithms are needed to make a traveler of robots with the least cost and without collisions. Many techniques have been developed in path planning for mobile robot worldwide, however, the most commonly used techniques are presented here for further study. This essay aims to review various path planning strategies for mobile robots using different optimization methods taken recent publisher’s paper in last five year.

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“…All algorithms are implemented in Matlab R2019b that runs on the Windows 10 platform in the Intel Core i7-6700 machine, 3.40 GHz CPU, and 16 GB of RAM. The following evaluation metrics are employed in [21,30,40,43,18] are implemented and recorded from the testing data in each run:…”

Section: Evaluation Criteriamentioning

confidence: 99%

ATS Drug Classification by Using Whale Optimization Based Descriptors

Chiranjeevi¹,

Naidu²,

Manohar³

et al. 2021

Preprint

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To improve the wrapper feature selection technique, swarm intelligence (SI) has been a preferred choice. The use of a binary whale optimization algorithm (BWOA) to handle the moleular descriptors selection problem in AMPHETAMINE-TYPE STIMULANTS (ATS) drug categorization has attracted this research. This work aims to improve the classifier's learning and prediction abilities in order to produce better classification results. BWOA are generated using S-shaped transfer functions, which are subsequently consolidated using a k-Nearest Neighbor (k-NN) classifier in the wrapper feature selection. Our goal is to see how different sigmoid transfer functions affect the significant feature selection and classification in BWOA. For performance assessment, several indicators and Wilcoxon's rank-sum test are used. The BWOA-S3 delivers performance improvements with the lowest fitness value, fast convergence, good classification accuracy, and a compact feature subset, according to experimental data. Three distinct classifiers also ratify the generalization of the best feature subset.

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Whale optimization algorithm: a systematic review of contemporary applications, modifications and developments

Cited by 178 publications

References 77 publications

Artificial Neural Synchronization Using Nature Inspired Whale Optimization

Artificial Neural Synchronization Using Nature Inspired Whale Optimization

A Review on Path Planning Algorithms for Mobile Robots

ATS Drug Classification by Using Whale Optimization Based Descriptors

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